Investigating the elastic deformation of wood–plastic composites at cold temperature using the bubble inflation technique
Bibliographic record
Abstract
In recent years, there has been increasing interest in the development of wood–plastic composites (WPC) for use as building materials. Using wood as natural reinforcement in composite materials, instead of mineral reinforcements, has several advantages such as low density, low cost, and less abrasive finish. The natural reinforcements are also non-toxic and recyclable. The wide variety of WPCs makes it difficult to discuss the performance of these composites. In this research, the bubble inflation technique, introduced by Joye et al., is used to study the behaviour of biocomposite materials in cold temperatures. The results of experiments with high-density polyethylene and WPC membranes with 20, 30, 40, 50 and 60 wt% of wood fibre, under the combined effect of temperature and pressure are presented. The membranes are tested under the maximum pressure of 18 psi within the temperature range of −50C to +50°C with 25°C increments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".